Measured Effects of Anthropogenic Development on Vertebrate Wildlife Diversity

1. Introduction

Urbanization has been defined as “the process of human settlement that gradually transforms uninhabited wildlands into lands including some degree of relatively permanent human presence” [1]. Urban growth profoundly affects the availability and condition of natural resources, and within its immediate area it often fragments and degrades habitat and simplifies biological species composition [2], as well as homogenizes species composition of plants [3] and wildlife [4,5,6,7,8] and land-cover composition, landscape structure, and ecosystem functions [9]. Urban areas also reduce avian taxonomic diversity [10,11]. In short, urbanization diminishes biodiversity, which is intrinsically valuable but also essential to human quality of life [11,12]. “Biodiversity is the variety of life” [11], ranging from the microbial to biome, from genetics to phenotypes, and including ecological processes of storages, flows and interactions. It is too complex to measure on the whole, so ecologists simplify measures of biodiversity with indicators such as populations, species and habitats, which are also better documented and understand than most other potential measures of biodiversity [12]. Biodiversity is on the decline [12]. A major driver of declining trends in biodiversity within metropolitan areas is the extent of landcover that serves as habitat [8].

Habitat is that part of the environment that is used by members of a species [13,14]. But when that part of the environment is converted to one or more buildings, associated impervious surfaces (parking lots), and lawn or ornamental vegetation as cover on the residual ground surface, along with all of the noise, light and air pollution and sources of mortality that accompany urban development, habitats of many species are lost [15]. In the context of a city or metropolitan area, habitat loss likely also includes habitat fragmentation which results in a net loss of productive capacity of a species that exceeds that of habitat loss alone [16]. Because habitat loss and habitat fragmentation have progressed rapidly around the world, the effects of these processes on wildlife are also advancing rapidly [17]. Already, there have been documented genetic effects [18], and shifts in community composition and in physical and behavioral morphologies of species remaining within the areas of urbanization [15,19].

Many species of vertebrate wildlife have been in numerical decline across North America [20]. These declines have been attributed to multiple causal factors, but habitat loss and habitat fragmentation have usually been hypothesized as the leading causes of declines. Habitat loss is readily believable because we can see and measure the extent to which we have been clearing natural vegetation to make way for agricultural, industrial, commercial and residential uses and all of their connecting roads and highways, pipelines and electrical transmission lines. Less measured, however, has been the actual changes in wildlife species composition and numerical abundance on sites where natural or managed vegetation has been removed to accommodate anthropogenic structures.

To indicate the effects of habitat loss due to urbanization, correlational analysis has been performed on bird species richness with variables intended to measure urbanization and degrees of departure from natural conditions [21,22,23,24]. In one study, birds were sampled in forest patches within an urban environment to test for correlations with patch size and degrees of disturbance [25]. Similarly, Crooks et al. [5] assessed the effects of urbanization by comparing bird species richness and abundance sampled in urban areas to samples taken from small and large habitat fragments and larger core areas of habitat. In another study, bird species richness was sampled along a gradient of urbanization, and it was assumed that the bird community sampled in the least urbanized end of the gradient best represented the pre-development bird community [26]. Investigators in another study estimated relative species richness of birds as an indicator of the effects of urbanization by comparing sampled species richness to a specified reference community or to the regional pool of species that should have existed prior to development [27]. The reference community would indicate a baseline ecological integrity, or the biological species assemblage during pristine conditions, sensu [28]. This approach, however, did not directly measure the effects of urbanization because it assumed the pristine reference community was accurately specified. The effects of habitat loss due to development have more often been assumed or inferred from gradient experiments, but perhaps directly measured only rarely [29,30].

One study that directly measured how construction of buildings affected bird species richness and abundance found no effect [30]. However, the study’s surveys were brief (10 min) and extended only to 30.5 m on a single study site. The study lacked the experimental design elements of replication, control, and sufficient scale needed to achieve the study’s objective. Another study that measured the effects of construction grading on the local woodland bird community initially documented equivocal impacts [29]. This study, however, provided greater insight into impacts because it’s sampling covered a larger area and twice the time as the other study. Grading displaced birds to adjacent habitat, where temporary crowding inflated the investigator’s bird counts. After another year, the effect of crowding was lost and a net loss of birds was realized [29]. Rare are long-term studies or experimental studies including controls to more directly test for the effects of urbanization [1].

Marzluff et al. [1] recommended that research to test for the effects of urbanization on wildlife should take advantage of the opportunities for experiments that urban development brings. Advanced knowledge of where and when the next projects are to be developed should give biologists opportunities to design experiments, which is just what Scott [29] did. We saw such an opportunity, discussed below, but we note that sites proposed for development are typically already far removed from the ecological integrity of pristine environmental conditions that preceded urbanization in the area. Realistically, the opportunities for experiment based on advanced knowledge of where development will extend must begin with baseline environmental settings that are highly disturbed or consist of habitat fragments in an urbanizing landscape [29]. What can be measured in such experiments are only partial, final-stage effects of urbanization on plants and wildlife.

We often survey for wildlife at sites that are proposed for development of anthropogenic structures in California. The California Environmental Quality Act (CEQA) requires the characterization of the existing environmental setting. This characterization informs the public and decision-makers of what is at stake in the face of the proposed project, and it serves as the baseline from which to opine on or predict project-caused impacts and to formulate appropriate mitigation measures to avoid, minimize, reduce, rectify or offset the impacts. To characterize the wildlife community as part of the existing environmental setting, consulting biologists usually perform what are referred to as reconnaissance surveys, otherwise known as general biological surveys. These surveys typically include one or more biologists walking over the project site or scanning the site from vantage points. The surveys vary in duration, typically lasting 1 to several hours. These surveys have not relied on fixed time-spans, and have lacked any clear stopping rules other than the biologists finished walking the pedestrian transects across the project area, whether those transects were evenly spaced or meandering. Having worked for environmental consulting firms in the past, in our experience the primary stopping rule is budget, although it was not always clear why a survey would come to an end at a particular site. We have seen no evidence that the stopping rules for reconnaissance surveys are results-based, as advocated by [31].

To check on the veracity of the results and interpretation of reconnaissance surveys completed by consulting biologists, we were often hired by parties other than the project applicants or the permitting agencies to also survey the project sites. One difference between the consultants’ surveys and ours is that the consultants surveyed directly on the project sites whereas we nearly always were constrained to survey from the project’s periphery because we were rarely granted access to the site’s interior. But this difference accommodated our post-construction, repeat surveys of sites that had been developed, because these developed sites could only be performed on the project site’s periphery due to the new physical presence of one or more buildings. As for the veracity of the survey findings of consulting biologists, they are of no relevance to the objectives of the study we present here.

We managed our surveys of project sites in the framework of a before-after, control-impact experiment to measure average project impacts to wildlife. Our control sites were those project sites that had not been developed prior to our second survey which represented the after phase of the experiment, and the impact sites were those sites that had been developed by the time of our second survey. Our baseline settings in the before-phase surveys were far from pristine at most project sites, but they nevertheless served as baselines for measuring changes brought by construction of buildings to the number of vertebrate species detected and to our counts of live animals, which we intended to indicate respectively as species richness and relative abundance. We use the term “indicate” here because we recognize that we did not truly measure species richness nor true abundance, as multiple potential biases and error prevented such measurement [32]. On the other hand, our surveys were of sufficient duration to detect most of the diurnal bird species that would have been available to us at each site at the times of our surveys [33]. Our primary objectives were to measure changes in 1) the number of vertebrate species detected, 2) the number of species uniquely detected at a site in one phase relative to the species detected in the other phase, 3) counts of live animals, and 4) percentage of project sites within each BACI treatment group where we detected each species. Our secondary objective was to explore whether other measured variables might explain the residual variation from the Analysis of Variance (ANOVA) model used to test the BACI hypotheses for main and interaction effects. We additionally tested for effects of survey duration, years intervening the surveys in the before and after phases, project area, latitude, degree of connectedness to adjacent open space, and whether the site was a redevelopment site, infill or not infill.

2. Materials and Methods

2.1. Study Area

Our study sites were clustered in the Sacramento and Central Valleys, the San Francisco Bay Area, and the coastal region of southern California (Figure 1). Each of these sites had been selected by our clients for reasons that had nothing to do with our BACI experiment. We later added follow-up surveys where practical and when we could closely match the date and start time of the initial surveys. Twenty-six of the sites had been developed by the time of our follow-up surveys, whereas 52 sites remained undeveloped. Developed sites were those for which the intended structures of the project were completely or nearly completely built, but they did not have to be occupied (some structures remained vacant for extended periods) (Figure 2, Figure 3 and Figure 4). Thirty-five of the sites were within or peripheral to existing urban, commercial or industrial areas, but 30 were located on agricultural or desert landscapes (Appendix A). The condition of most of our study sites was poor to moderate at the times of our initial surveys, as most sites had been disturbed by mechanical clearing of vegetation (Figure 5), use of the sites as construction laydown areas in support of development of adjacent properties, frequent fires, off-road vehicle use, non-native plant species composition (Figure 6), encroachment of non-native vertebrate species from surrounding urbanized areas (Figure 7), and other forms of pollution, e.g., dumping of waste materials and mowing for weed abatement. Only 4 of the sites were surrounded on all sides by open space. Nevertheless, wildlife utilized all of the study sites we surveyed. Study sites ranged from 0.526 ha to 1,549 ha (mean = 91.26 ha, SD = 255.44 ha), with the 2 largest study sites consisting of natural reserves that we used as control sites.

2.2. Reconnaissance Surveys

We performed what are referred to in California as reconnaissance surveys, also known as general biological surveys. We intentionally implemented the same methodology as used by environmental consultants when they perform reconnaissance surveys. In these surveys, all species are recorded if detected by visual or auditory means or by sign such as burrows or tracks or scat. Wildlife recorded included birds, mammals, amphibians and reptiles. We surveyed by walking the perimeter of the site, or by a stationary vantage point, where we scanned for wildlife with the use of binoculars. In most surveys, we recorded the time within the survey when a new species was detected, whether the species was on the project site or in the surrounding area, and the approximate abundance of that species.

Reconnaissance surveys are unbound by time, but typically last between 1 and several hours. Our surveys differed from the surveys of environmental consultants only in three ways: (1) we were usually restricted from accessing the interiors of project sites, (2) we used full-frame cameras fitted with telephoto lenses to help detect and identify wildlife to species, and (3) our stopping rule was results-based, and increasingly so the more surveys we had completed; however, in the cases of our second surveys to represent the after phase of the experiment, our stopping rule was the time it took to record all of the species that had been recorded in each initial survey. Experience with these surveys led us to stop our initial survey at each project site once our species detection rate declined to about one new species per 20 or 30 minutes, similar to the rule advocated by Watson [31]. These lower detection rates typically coincided with increasing heat of the day or oncoming darkness of the evening.

We recorded temperature, wind and sky condition at the start and end of most surveys, and we recorded ground conditions at all sites at the time of each survey. We categorized an urban setting index for each site as 0 = largely non-urbanized, 1 = urban infill, and 2 = redevelopment. We rated connectivity of project site borders to adjacent open space (including agriculture) as 0%, 10%, 25%, 50%, 75%, and 100%. We categorized sites as having undisturbed vegetation; evidence of disturbance over the last 5 years or so; ruderal; mowed; neglected by accumulation of trash, construction debris, waste soil or machine parts; neglected by cessation of irrigation; burned; disked; graded; construction ongoing; or constructed, as well as combinations of the foregoing categories. From these categories, we rated sites for level of disturbance as 1 = natural and biologically intact with no more than small patches of non-native vegetation; 2 = mostly intact with some native and some non-native vegetation, or all native with some past ground disturbance; 3 = modified (disked or highly disturbed) in the past but with substantial extent of vegetation such as patches of shrubs or scattered trees; 4 = landscaped parks or golf courses; 5 = agriculture including orchards and vineyards; 6 = agriculture including row crops; 7 = parking lot with mature shrubs or trees, and where buildings do not cover the entire site; 8 = highly modified with little vegetation remaining; 9 = compacted, pervious ground with no vegetation remaining; 10 = impervious ground with no vegetation remaining; 11 = constructed buildings. We further categorized site conditions to represent the intensity of actions that likely would have suppressed wildlife as 0 = none evident, 1 = low (ruderal, cleared fire break), 2 = routine disturbance, 3 = moderate (mowed, neglected), 4 = earlier intense (near-recent grading, regrowth after disking), 5 = intense (cleared, disked, disked and neglected), and 6 = very intense (converted to crop, graded, constructed).

Beginning in January 2020, we began to resurvey sites of proposed building projects that we had originally surveyed during the same time of year and about the same time of day between 1 and 19 years earlier. In addition to starting the repeat survey as close to the original start time as possible, we surveyed for the same duration and using the same person or both of us where we had originally surveyed together. Fifty-two of the project sites we resurveyed had remained undeveloped and 26 had been developed since our initial survey. Upon each repeat survey, we assigned sites that remained undeveloped to the control group, and sites that were since developed to the impact group. We compared our survey outcomes in a BACI experimental design. One metric of survey outcome was total number of vertebrate species seen during the survey, including species seen in the project area but off the project site. A second metric was total number of species seen only on the project site. A third metric the number of species uniquely detected at a site in one phase relative to the other phase. A fourth metric was total number of live animals counted during the survey (excluding fossorial mammals indicated by signs of burrow activity). A fifth metric was the number of sites at which a species or group of species was detected. For each metric, we quantified the expected outcome at impact sites (E_IA) relative to the before-after change in outcomes at the control site, and the effect of the development in the impact treatment level:

$$\begin{gathered} E_{IA}=\left(\frac{CA}{CB}\right)\times IB \\ Effect=\frac{(IA-E_{IA})}{E_{IA}}\times 100\%. \end{gathered}$$

Whereas most of our surveys adhered to our methodological protocol, a few did not. At the Rider Warehouse site, we did not quantify our defined metrics from the unconstrained viewshed, because our survey extended too far beyond the Rider project footprint and therefore included too many animals that were less likely to have been directed affected by the project. We did, however, quantify our metrics for the on-site comparisons, because we had carefully noted which species were on site. Another deviation from our normal methodology was one of us (NLS) surveyed the site alone in the after phase, whereas both of us surveyed the site in the before phase. And finally, the season of the second survey did not match the season of the first (March instead of July), because the second survey was in response to a client request to survey the adjacent property and had to be completed in March 2021.

Whereas KSS surveyed the Brawley Solar Energy Facility by himself in 2022, both of surveyed it in 2023. And whereas both of surveyed the Olympic Holding Inland Center Warehouse in 2019, NLS surveyed it alone in 2021.

The Casmalia and Linden site was used twice in the experiment, once as a control site and then as an impact site. NLS surveyed this site twice before it was developed into a warehouse, so the first two surveys represented the control treatment. She completed a third survey after the site was developed, so based on the second and third surveys we also treated the site as an impact treatment.

Because it was not until 2021 when we began consistently recording whether animals were detected strictly on a project site or just off site, the sample sizes of our metrics measured on site are smaller than those of our metrics measured within an unconstrained viewshed. We note, also, that our unconstrained viewsheds were actually constrained to within a distance that we judged was of no biological consequence to the species involved; in other words, the animals that were recorded were close enough to the project site to readily make use of the project site.

Several of the control sites were not actually proposed as project developments, but were used for their convenience and proximity to impact sites. These control sites included the Hayward Regional Shoreline, which was adjacent to the Winton Avenue Industrial Project. Another such site was Bachman Canyon, which was adjacent to University of California San Diego’s Hillcrest Campus, where a project was proposed and underwent development. We also surveyed the Spenceville Wildlife Management and Recreation Area, which was adjacent to a proposed residential development that was not approved. We chose to survey the Spenceville site because we were not allowed access to the adjacent project site.

2.3. Hypothesis Tests

We compared rates of species detections with increasing time into the survey by the following experimental treatment groups: control-before (CB), control-after (CA), impact-before (IB) and impact-after (IA). To arrive at these comparisons, we fit a nonlinear least-squares regression model to the cumulative number of species detected, Y, as a function of the number of minutes, X, into each survey. The form of the model was $Y=\frac{1}{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$a$}\right.+b\times {(X+1)}^{-c}}$ , where X represents minutes into the survey, and a, b, and c were best-fit coefficients. Because the surveys varied in duration, this modeling approach was also useful for minimizing the effect of survey duration on the metric, number of species detected. We did this by using each model to predict the number of species that would be detected after one hour. We chose 1 hour because it was within the data range of all but 4 of the surveys we completed.

We used 2-factor analysis of variance with interest mostly in the significance of the interaction effect between before-after time period (BA) and control-impact treatment (CI) of each BACI experiment, as implemented by others [34,35]. Except for our model-predicted number of species detected after one hour of survey, we log₁₀-transformed count variables such as numbers of species detected and number of animals counted. To determine whether log₁₀-transformed data met the assumptions of ANOVA, we visually examined normal probability plots, and we performed Hartley’s F-max test for homogeneity of variance. The assumptions were met in nearly every test. To further assess 2-factor ANOVA interaction effects, we calculated their statistical power.

2.4. Other factors

From the BACI test of the number of vertebrate species detected, we saved ANOVA model residuals for exploring whether additional variation in the data could be explained by factors represented by other measured variables. We tested for additional relationships with project size (ha), latitude, number of years between the surveys in the Before and After phases, the comparable survey duration (minutes of survey), and similarity index [36]. We note that whereas the similarity index was intended to measure the similarity of community composition of constituent species, its true measurement must also be of species detection probabilities attributable to the surveys.

2.5. Species characteristics

We compared the species detected among surveys to identify the frequency that each was found in the Before and After phases and between the Control and Impact treatment levels. We further grouped species into classes including amphibians, reptiles, mammals, birds, grassland birds, raptors, synanthropic birds, non-native species, and special-status species. The latter class was informed by legal protections afforded species by state and federal statutes and by designations assigned to species by state and federal wildlife agencies. (Species names, species groupings and special-status are listed in Table 4.) We measured development impacts to these classes by mean number of species within each that was detected per survey.

3. Results

Impact sites differed from control sites in a number of ways, including their average smaller size, lower elevation, and 94 km more northerly locations (Table 1). On average, impact sites were half to less than half connected to open space as compared to control sites. Impact sites also ranked higher on the urban setting index, which meant they were more likely to be infill or redevelopment projects Furthermore, impact sites rated higher for level of disturbance, even prior to development, and they ranked higher on the intensity of actions resulting in suppression of wildlife occurrences, even prior to development (Table 1). On average, survey duration was briefer on impact sites by nearly half an hour, the time between the first and second surveys was longer by 1.3 years, but the average difference in start time was insubstantial.

3.1. BACI Experiment

As part of our experiment, we completed 77 pairs of before and after surveys or 154 surveys. Our cumulative number of species detections increased with increasing survey duration, but the rates of these increases differed between sites in the control and impact treatment levels, and the mean rate was slowest among sites in the Impact-After group, i.e., the sites that had been developed (Figure 8a). The model-predicted number of species detected by 1 hour into a survey averaged about 10.4 in the IA group as compared to 20.8 to 21.8 in the CB and CA groups. By 5 hours, the disparity increased to 12.7 species detected in the IA group as compared to 37.6 in the CB and CA groups (Figure 8b). At 1 hour, the model-predicted number of species detected was 51% lower in the IA group, but at 5 hours it was 66% lower.

We observed large changes in species composition and relative abundance among the project sites that were developed before our second survey. Some of the species we detected in the before phase were relatively abundant, but their abundance declined sharply after development. For example, at the Scannell Properties project site in Richmond, California, our before and after counts changed from 100 to 0 house finches, and from 70 to 17 European starlings. Where construction commenced on the University of California San Diego’s Hillcrest Campus, our before and after counts changed from 25 to 0 Anna’s hummingbirds. At the Centerpoint Warehouse Project site in Manteca, our before and after counts changed from 300 to 9 American crows, from 40 to 3 mourning doves, from 400 to 0 western meadowlarks, and from 30 to 0 house finches. At the Scheu Warehouse Project site in Rancho Cucamonga, our before and after counts changed from 300 to 1 mourning doves, and from 40 to 0 European starlings. On average, we also counted 88% fewer vertebrate animals, including 85% fewer animals of special-status species, on impact sites after development, and we detected 44% fewer vertebrate species on impact sites after development, and 62% fewer vertebrate species on the project footprint (Table 2).

We also observed changes in species composition and relative abundance among the project sites that did not undergo development and which we treated as our control sites. For example, at the Haun and Holland Project site in Menifee, our before and after counts shifted from 20 to 10 European starlings, 7 to 40 house finches, 10 to 3 desert cottontails and from 97 to 175 vertebrate animals. At the Gillespie Field Project site in El Cajon, our before and after counts shifted from 122 to 176 vertebrate animals. At the Operon HKI Project site in Perris, our before and after counts shifted from 10 to 20 savannah sparrows (Figure 9), 0 to 20 western meadowlarks, 0 to 20 horned larks. And at the PARS Global Storage Project site in Murietta, our before and after counts shifted from 26 to 75 vertebrate animals, including from 3 to 6 Anna’s hummingbirds, 5 to 10 American crows, 0 to 25 bushtits, and 0 to 10 American crows. On average, we detected about 2–3 additional species in most groups of species during our second surveys among the control sites, and we counted about 26% more birds, but notably we counted 56% fewer species and we counted 44% fewer animals of special-status species vertebrate wildlife (Table 2).

In the before phase, the number of species detected averaged fewer at the impact sites as compared to the controls, whereas the number of animals counted averaged more at the impact sites compared to the controls (Table 2). Consequently, the control-impact main effects were significant for all but one of the metrics consisting of number of species detected, whereas they were not significant for any of the metrics of the number of animals counted (Table 3). The before-after main effects were significant for the number of vertebrate species detected and the number of birds detected, but not for the numbers of species detected of mammals or reptiles and amphibians. The before-after main effects were significant for all of the metrics of the number of animals counted. But whereas these main effects point towards potential biases, our main interest was in the interaction effect, which informs of the impact of the action (development), and which presumably would have been largely controlled for in the experiment.

After development, the average number of vertebrate wildlife species we detected declined 48% within the project area, and 66% within the bounds of the project sites (Table 2, Figure 10). These declines were significant (Table 3) The average number of bird species declined 45% within the project area, and 64% within the bounds of the project sites (Table 2). These declines were also significant (Table 3). Although not significant due to insufficient statistical power (Table 3), the average number of mammal species declined 79% across the entire viewshed and 92% within the bounds of the project sites, and the average number of amphibians and reptiles (“herps”) declined 47% in the project area, and 100% within the bounds of the project sites (Table 2).

After development, the average number of vertebrate animals we counted declined 90% within the project area (Figure 11a), and 89% within the bounds of the project sites (Table 2). These declines were significant (Table 3). The average number of birds we counted declined 91% within the project area (Table 2, Figure 11b), and 89% within the bounds of the project sites (Table 2), both of which were significant (Table 3).

After development, the average number of special-status species declined 49% within the project area, and 58% within the bounds of the project sites (Table 2, Figure 12a). The average number of vertebrate animals of special-status species that we counted declined 70% within the project area, and 82% within the bounds of the project sites (Table 2, Figure 12b). All of these declines were significant (Table 3).

After development, the average model-predicted number of vertebrate species detected in one hour of survey declined 37% within the project area (Table 2, Figure 13a), which was significant (Table 3). The number of vertebrate species uniquely detected at a site in one phase relative to the other phase declined 74% (Table 2, Figure 13b), which was also significant (Table 3).

3.2. Other factors

We found that the ANOVA model residuals increased significantly with increasing survey duration (Figure 14a). We therefore used the ANOVA residuals from the BACI experiment involving model-predicted numbers of vertebrate species detected after one hour of survey, assuming that the residuals from this test would most effectively minimize any residual variation of survey duration. The model-derived residuals continued to increase with increasing survey duration (Figure 14b), but with a much smaller r², a smaller standardized slope coefficient, β, and a slightly larger root-mean squared error (RMSE). We did not entirely eliminate the effects of survey duration, but we reduced it substantially.

Model-adjusted residuals related only weakly with multiple variables, including with the intensity of pre-survey actions that would have suppressed wildlife (F_6,125 = 1.14, P = 0.3437), project size (ha), latitude, the number of years between the surveys in the before and after phases, and the similarity index measured between before and after surveys at the same site. But they differed significantly among groups of sites located in open space or in an infill setting or as redevelopment within developed areas such as cities (Figure 15). Mean residuals were positive within open areas, and negative in areas of infill or redevelopment.

Model-adjusted residuals also differed significantly by levels of connectedness to open space (Figure 15b). Mean residuals were positive among sites with >50% connectivity to open space, and negative among sites with <50% connectivity to open space.

3.3. Species characteristics

A few species of wildlife increased in frequency of detection among project sites that were developed including in order of increase, Cooper’s hawk, ruby-crowned kinglet, yellow-rumped warbler, California gull, black phoebe, house cat, and Anna’s hummingbird (Table 4). Many more species, however, decreased in frequency of detection, including in order of decrease, California ground squirrel, Botta’s pocket gopher, burrowing owl, California quail, California vole, Cassin’s kingbird, cedar waxwing, coyote, great-tailed grackle, killdeer, loggerhead shrike, northern rough-winged swallow, oak titmouse, orange-crowned warbler, Sierran treefrog, white-tailed kite, white-throated swift, and yellow-billed magpie, followed by western meadowlark, red-winged blackbird, western fence lizard, great egret, American robin, eastern gray squirrel, mallard, American kestrel, red-tailed hawk, white-crowned sparrow, black-tailed jackrabbit, dark-eyed junco, western gull, savannah sparrow, European starling, California towhee, bushtit, lesser goldfinch, Brewer’s blackbird, Canada goose, northern flicker, turkey vulture, Nuttall’s woodpecker, barn swallow, western kingbird, Swainson’s hawk, rock pigeon, mourning dove, red-shouldered hawk, double-crested cormorant, house finch, Eurasian collared-dove, California scrub-jay, cliff swallow, house sparrow, desert cottontail, northern mockingbird, common raven, American goldfinch, Say’s phoebe, and American crow (Table 4).

Groups of wildlife that declined the most following development included, in the following order, Amphibians, mammals (Figure 16), grassland birds, raptors, special-status species, all birds as a group, non-native birds, and synanthropic birds (Table 5).

Table 4. Frequency of detection of each species among the surveys in the experimental treatment groups of control-before, control-after, impact-before and impact-after. Measures of effect of development appear in the right column for those species with sufficient sample sizes or special status.

Species	Scientific name	Type1	Status2	Control (n = 52)		Impact (n = 26)		Effect (%)
				Before	After	Before	After
Abert's towhee	Melozone aberti			1	1	0	0
Acorn woodpecker	Melanerpes formicivorus			6	12	0	0
Alameda song sparrow	Melospiza melodia pusillula		BCC, SSC2	1	0	0	0
Allen's hummingbird	Selasphorus sasin		BCC	9	10	0	0
American avocet	Recurvirostra americanus		BCC*	0	1	0	0
American beaver	Castor canadensis			0	1	0	0
American bittern	Botaurus lentiginosus			1	0	0	0
American coot	Fulica americana			3	5	0	0
American crow	Corvus brachyrhynchos	S		41	38	24	21	-6
American goldfinch	Spinus tristis			18	10	8	4	-10
American kestrel	Falco sparverius	R	BOP	20	22	14	5	-68
American pipit	Anthus rubescens	G		4	9	1	0
American robin	Turdus migratorius			15	12	5	1	-75
American wigeon	Mareca americana			1	3	0	0
Anna's hummingbird	Calypte anna	S		35	40	12	15	9
Ash-throated flycatcher	Myiarchus cinerascens			5	5	0	1
Baja California treefrog	Pseudacris hypochondriaca			1	2	0	0
Bald eagle	Haliaeetus leucocephalus	R	CE, BGEPA, CFP	1	1	0	1
Band-tailed pigeon	Patagioenas fasciata			1	1	0	0
Barn swallow	Hirundo rustica			9	13	5	4	-45
Bat sp.				1	0	0	0
Belted kingfisher	Ceryle alcyon			2	2	0	0
Bewick's wren	Thryomanes bewickii			10	7	1	0
Black-chinned hummingbird	Sayornis nigricans			3	1	0	0
Black-crowned night-heron	Nycticorax nycticorax			2	0	1	1
Black-headed grosbeak	Archilochus alexandri			2	1	1	0
Black-necked stilt	Nycticorax nycticorax			3	4	0	0
Black-tailed gnatcatcher	Pheucticus melanocephalus		TWL	1	1	0	0
Black-tailed jackrabbit	Himantopus mexicanus			6	3	6	1	-67
Black-throated gray warbler	Polioptila melanura			0	1	1	0
Black phoebe	Lepus californicus	S		30	22	11	9	12
Blue-gray gnatcatcher	Passerina caerulea			3	4	0	0
Blue grosbeak	Polioptila caerulea			0	1	2	0
Bobcat	Felis rufus			4	2	0	0
Botta's pocket gopher	Thomomys bottae			24	31	8	0	-100
Brewer's blackbird	Euphagus cyanocephalus			9	13	6	4	-54
Brewer's sparrow	Spizella breweri			0	1	0	0
Broad-footed mole	Scapanus latimanus			0	2	0	0
Brown-headed cowbird	Molothrus ater			9	3	3	1	0
Bryant's Savannah sparrow	Passerculus sandwichensis alaudinus	G	SSC3	1	0	0	0
Bryant's woodrat	Neotoma bryanti			1	2	0	0
Bufflehead	Bucephala albeola			2	1	0	0
Bullock's oriole	Icterus bullockii		BCC	3	2	1	0
Burrowing owl	Athene cunicularia	R, G	BCC, SSC2, BOP	3	1	1	0	-100
Bushtit	Psaltriparus minimus			19	21	4	2	-55
Cactus wren	Campylorhynchus brunneicapillus			1	1	0	0
California brown pelican	Pelicanus occidentalis californicus		CFP	1	2	0	0
California gnatcatcher	Polioptila c. californica		FT, SSC2	4	1	0	0
California ground squirrel	Otospermophilus beecheyi			18	24	8	0	-100
California gull	Larus californicus		BCC, TWL	22	15	11	10	33
California horned lark	Eremophila alpestris actia	G	TWL	1	3	0	0
California quail	Callipepla californica			6	10	3	0	-100
California scrub-jay	Aphelocoma californica			19	25	10	9	-32
California thrasher	Toxostoma redivivum		BCC	4	3	0	0
California towhee	Melozone crissalis			19	18	5	2	-58
California vole	Microtus californicus			5	5	2	0	-100
Calliope hummingbird	Stellula calliope			1	0	0	0
Canada goose	Branta canadensis			12	19	5	4	-50
Cassin's kingbird	Tyrannus vociferans			8	10	2	0	-100
Cattle egret	Bubulcus ibis			1	1	0	0
Cedar waxwing	Bombycilla cedrorum			3	4	3	0	-100
Chestnut-backed chickadee	Poecile rufescens			6	6	0	1
Chipping sparrow	Spizella passerina			1	1	1	0
Cinnamon teal	Spatula cyanoptera			1	2	0	0
Cliff swallow	Petrochelidon pyrrhonota			10	14	4	4	-29
Common goldeneye	Bucephala clangula			1	0	0	0
Common ground dove	Columbina passerina			1	0	0	0
Common merganser	Mergus merganser			1	0	0	0
Common raven	Corvus corax	S		33	34	11	10	-12
Common yellowthroat	Geothlypis trichas			2	5	0	0
Cooper's hawk	Accipiter cooperii	R	TWL, BOP	13	10	3	4	73
Costa's hummingbird	Calypte costae		BCC	1	0	0	0
Coyote	Canis latrans			15	11	2	0	-100
Dark-eyed junco	Junco hyemalis			7	7	3	1	-67
Deer mouse	Peromyscus maniculatus			0	1	0	0
Desert cottontail	Sylvilagus audubonii			12	7	2	1	-14
Domestic dog	Canis familiaris			2	0	0	0
Double-crested cormorant	Nannopterum auritum		TWL	13	10	4	2	-35
Downy woodpecker	Dryobates pubescens			2	5	1	0
Eared grebe	Podiceps nigricollis			0	1	0	0
Eastern fox squirrel	Sciurus niger			2	3	0	0
Eastern gray squirrel	Sciurus carolinensis			3	4	3	1	-75
Egyptian goose	Alopochen aegyptiacus			0	0	0	1
Eurasian collared-dove	Streptopelia decaocto	S		18	23	15	13	-32
European starling	Sturnus vulgaris	S		28	35	20	10	-60
Evening grosbeak	Coccothraustes vespertinus			0	1	1	0
Ferruginous hawk	Buteo regalis	R	TWL, BOP	0	1	2	0
Forster's tern	Sterna forstreri			1	2	0	1
Fox sparrow	Passerella iliaca			0	0	1	0
Gadwall	Anas strepera			1	1	0	0
Gambel's quail	Callipepla gambelii			1	1	0	0
Glaucous-winged gull	Larus glaucescens			1	1	1	0
Golden eagle	Aquila chrysaetos	R	BGEPA, CFP, BOP, TWL	0	2	0	0
Golden-crowned sparrow	Zonotrichia atricapilla			1	4	1	0
Gopher snake	Pituophis melanoleucus			1	0	0	0
Granite spiny lizard	Sceloporus orcutti			1	1	0	0
Grasshopper sparrow	Ammodramus savannarum	G	SSC2	1	1	0	0
Gray fox	Urocyon cinereoargenteus			2	1	0	0
Great Basin fence lizard	Sceloporus occidentalis longipes			2	2	0	0
Great blue heron	Ardea herodias			9	7	1	0
Great egret	Ardea alba			17	12	7	1	-80
Great horned owl	Bubo virginianus	R	BOP	5	3	0	0
Greater roadrunner	Geococcyx californianus			1	3	0	0
Greater white-fronted goose	Anser albifrons			1	0	0	0
Greater yellowlegs	Tringa melanoleuca			2	1	0	0
Great-tailed grackle	Quiscalus mexicanus			3	2	3	0	-100
Green heron	Butorides virescens			1	1	0	0
Hairy woodpecker	Dryobates villosus			3	4	0	0
Harbor seal	Phoca vitulina			1	1	0	0
Herring gull	Larus argentatus			2	0	2	0
Hooded merganser	Lophodytes cucullatus			0	1	0	0
Hooded oriole	Icterus cucullatus			3	5	0	0
Horned grebe	Podiceps auritus			2	0	0	0
Horned lark	Eremophila alpestris	G		4	7	0	0
House cat	Felis catus			5	3	3	2	11
House finch	Haemorphous mexicanus	S		45	46	18	12	-35
House sparrow	Passer domesticus	S		10	9	7	5	-21
House wren	Troglodytes aedon			1	2	0	0
Hutton's vireo	Vireo huttoni			3	1	1	0
Kangaroo rat	Dipodomys sp.			4	4	0	0
Killdeer	Charadrius vociferus	G		16	13	9	0	-100
Large-billed savannah sparrow	Passerculus sandwichensis rostratus		SSC2	1	0	0	0
Lark sparrow	Chondestes grammacus			2	3	0	1
Lawrence's goldfinch	Spinus lawrencei		BCC	4	2	0	0
Lazuli bunting	Passerina amoena			1	2	0	0
Least sandpiper	Calidris minutilla			1	2	0	0
Lesser goldfinch	Spinus psaltria	S		22	32	6	4	-54
Lesser nighthawk	Chordeiles acutipennis			1	2	0	0
Lesser scaup	Aythya affinis			1	0	0	0
Lesser yellowlegs	Tringa flaviceps			0	1	0	0
Lincoln's sparrow	Melospiza lincolnii			7	10	1	0
Loggerhead shrike	Lanius ludovicianus	G	SSC2	2	2	3	0	-100
Long-billed curlew	Numenius americanus		TWL, BCC*	1	0	0	0
Long-tailed weasel	Mustella frenata			1	0	0	0
MacGillivray's warbler	Geothlypus tolmiei			1	2	0	0
Mallard	Anas platyrhynchos			17	19	3	1	-70
Marsh wren	Cistothorus palustris			3	2	0	0
Merlin	Falco columbarius	R	TWL, BOP	4	1	1	0
Merriam's chipmunk	Neotamias merriami			1	0	0	0
Mew gull	Larus canus			1	0	0	0
Mouse sp.				1	0	0	0
Mountain bluebird	Sialia currucoides			1	0	0	0
Mountain chickadee	Parus gambeli			1	1	0	0
Mountain lion	Puma concolor		CCT	1	1	0	0
Mourning dove	Zenaida macroura	S		43	43	21	12	-43
Mule deer	Odocoileus hemionus			7	6	0	0
Muskrat	Ondatra zibethicus			1	0	0	0
Mute swan	Cygnus olor			1	1	1	0
Nashville warbler	Vermivora ruficapilla			1	1	0	0
Northern flicker	Colaptes auratus			10	13	6	4	-49
Northern harrier	Circus hudsonius	R, G	BCC, SSC3, BOP	8	7	0	1
Northern mockingbird	Mimus polyglottos	S		27	25	16	13	-12
Northern pintail	Anas acuta			1	0	0	0
Northern rough-winged swallow	Stelgidopteryx serripennis			15	9	4	0	-100
Nutmeg mannikin	Lonchura punctulata			2	2	0	0
Nuttall's woodpecker	Dryobates nuttallii		BCC	8	15	1	1	-47
Oak titmouse	Baeolophus inornatus		BCC	5	11	1	0
Olive-sided flycatcher	Contopus cooperi		BCC, SSC2	3	2	0	0
Orange-crowned warbler	Leiothlypis celata			3	6	1	0
Osprey	Pandion haliaetus	R	TWL, BOP	2	2	1	1
Pacific-slope flycatcher	Empidonax difficilis			1	2	0	0
Pacific wren	Troglodytes pacificus			1	2	0	0
Pelagic cormorant	Phalacrocorax pelagicus			0	1	0	0
Peregrine falcon	Falco peregrinus	R	CFP, BOP	5	3	1	1
Phainopepla	Phainopepla nitens			2	3	0	0
Pied-billed grebe	Podilymbus podiceps			2	1	0	0
Prairie falcon	Falco mexicanus	R, G	TWL, BOP	1	0	1	0
Purple finch	Haemorhous purpureus			2	2	1	0
Pygmy nuthatch	Sitta pygmaea			1	2	0	0
Raccoon	Procyon lotor			3	3	1	0
Red-breasted nuthatch	Sitta canadensis			1	3	0	0
Red-breasted sapsucker	Sphyrapicus ruber			1	0	0	0
Red-masked parakeet	Psittacara erythrogenys			0	1	0	0
Red-necked phalarope	Phalaropus lobatus			0	1	0	0
Red-shouldered hawk	Buteo lineatus	R	BOP	7	3	4	1	-42
Red-tailed hawk	Buteo jamaicensis	R	BOP	35	44	17	7	-67
Red-winged blackbird	Agelaius phoeniceus			14	16	6	1	-85
Red fox	Vulpes vulpes			1	1	0	0
Ring-billed gull	Larus delawarensis			4	7	0	0
Ring-necked pheasant	Phasianus colchicus			2	0	0	0
Rock pigeon	Columba livia	S	Non-native	21	31	18	15	-44
Rose-ringed parakeet	Psittacula krameri			0	2	0	0
Ruby-crowned kinglet	Regulus calendula			6	12	1	3	50
Ruddy duck	Oxyura jamaicensis			0	1	0	0
Rufous hummingbird	Selasphorus rufus		BCC			1	0
San Diegan tiger whiptail	Aspidoscelis tigris stejnegeri		SSC	1	0	0	0
San Francisco common yellowthroat	Geothlypis trichas sinuosa		BCC, SSC3	1	1	0	0
San Francisco dusky-footed woodrat	Neotoma fuscipes annectens		SSC	1	1	0	0
Savannah sparrow	Passerculus sandwichensis	G		13	14	5	2	-63
Say's phoebe	Sayornis saya	G		21	15	6	4	-7
Sharp-shinned hawk	Accipiter striatus	R	TWL, BOP	1	2	0	1
Short-billed dowitcher	Limnodromus griseus		BCC	1	0	0	0
Short-eared owl	Asio flammeus	R	BCC, SSC3, BOP	2	0	0	0
Side-blotched lizard	Uta stansburiana			1	0	0	0
Sierran treefrog	Pseudacris sierra			3	9	1	0	-100
Snow goose	Chen caerulescens			1	1	0	0
Snowy egret	Egretta thula			3	4	0	1
Song sparrow	Melospiza melodia			13	15	0	0
Sora	Porzana carolina			1	0	0	0
Southern alligator lizard	Gerrhonotus multicarinatus			2	1	0	0
Southern California rufous-crowned sparrow	Aimophila ruficeps canescens		TWL	2	0	0	0
Southern mule deer	Odocoileus hemionus fuliginatus			1	1	0	0
Southern Pacific rattlesnake	Crotalus oreganus helleri			1	1	0	0
Southern sagebrush lizard	Sceloporus graciosus vandenburgianus			1	0	0	0
Spotted sandpiper	Actitis macularius			1	1	0	0
Spotted towhee	Pipilo maculatus			10	11	0	0
Steller's jay	Cyanocitta stelleri			4	3	0	0
Striped skunk	Mephitis mephitis			3	3	0	0
Surf scoter	Melanitta perspicillata			0	1	0	0
Swainson's hawk	Buteo swainsoni	R	CT, BOP	5	3	3	1	-44
Swainson's thrush	Catharus ustulatus			1	1	0	0
Townsend's warbler	Setophaga townsendi			1	2	1	0
Tree swallow	Tachycineta bicolor			6	6	0	1
Tricolored blackbird	Agelaius tricolor	G	CT, BCC, SSC1	2	0	0	0
Tundra swan	Cygnus columbianus			1	0	0	0
Turkey vulture	Cathartes aura	R	BOP	17	21	11	7	-49
Vaux's swift	Chaetura vauxi		SSC2, BCC	0	1	0	0
Verdin	Auriparus flaviceps		BCC	1	1	0	0
Vermilion flycatcher	Pyrocephalus rubinus	G	SSC2	1	0	0	0
Violet-green swallow	Tachycineta thalassina			3	4	0	1
Virginia opossum	Didelphis virginianus			2	0	0	0
Warbling vireo	Vireo gilvus			2	1	0	0
Western bluebird	Sialia mexicana			9	12	0	0
Western fence lizard	Sceloporus occidentalis			2	6	2	1	-83
Western gray squirrel	Sciurus griseus			3	5	0	1
Western gull	Larus occidentalis		BCC	2	6	1	1	-67
Western kingbird	Tyrannus verticalis			10	9	4	2	-44
Western meadowlark	Sturnella neglecta	G		18	23	11	2	-86
Western sandpiper	Calidris mauri			0	1	0	0
Western screech-owl	Megascops kennicottii	R	BOP	1	0	0	0
Western side-blotched lizard	Uta stansburiana elegans			4	5	0	0
Western tanager	Piranga ludoviciana			2	2	0	0
White-breasted nuthatch	Sitta carolinensis			4	6	0	0
White-crowned sparrow	Zonotrichia leucophrys			17	23	9	4	-67
White-faced ibis	Plegadis chihi		TWL	1	3	1	0
White-tailed kite	Elanus leucurus	R	CFP, BOP	14	7	7	0	-100
White-throated swift	Aeronautes saxatalis			5	3	2	0	-100
White-winged dove	Zenaida asiatica			1	0	0	0
Wild turkey	Meleagris gallopavo			6	7	2	0	-100
Willet	Tringa semipalmata		BCC	3	3	0	0
Willow flycatcher	Empidonax traillii		CE	0	1	1	0
Wilson's snipe	Gallinago delicata			0	2	1	0
Wilsons warbler	Wilsonia pusilla			3	6	0	0
Wood duck	Aix sponsa			1	0	0	0
Wrentit	Chamaea fasciata		BCC	5	6	0	0
Yellow-billed magpie	Setophaga petechia		BCC	2	1	3	0	-100
Yellow-headed blackbird	Pica nuttalli	G	SSC3	1	0	0	0
Yellow-rumped warbler	Xanthocephalus xanthocephalus	S		21	21	7	10	43
Yellow warbler	Setophaga coronata		SSC2	5	6	0	2

¹ G = grassland bird, R = raptor, S = synanthrope. ² FT or FE = federal threatened or endangered, FC = federal candidate for listing as threatened or endangered, BGEPA = Bald and Golden Eagle Protection Act, BCC = U.S. Fish and Wildlife Service’s Birds of Conservation Concern, CT or CE = California threatened or endangered, CCT or CCE = Candidate California threatened or endangered, CFP = California Fully Protected (California Fish and Game Code 3511), SSC = California Species of Special Concern (not threatened with extinction, but rare, very restricted in range, declining throughout range, peripheral portion of species' range, associated with habitat that is declining in extent), SSC₁, SSC₂ and SSC₃ = California Bird Species of Special Concern priorities 1, 2 and 3, respectively (Shuford and Gardali 2008), TWL = California Taxa to Watch List, and BOP = Birds of Prey (California Fish and Game Code 3503.5). ^* Uncertain of range of BCC status based on 2021 Bird of Conservation Concern list.

Table 4. Frequency of detection of each species among the surveys in the experimental treatment groups of control-before, control-after, impact-before and impact-after. Measures of effect of development appear in the right column for those species with sufficient sample sizes or special status.

Species	Scientific name	Type1	Status2	Control (n = 52)		Impact (n = 26)		Effect (%)
				Before	After	Before	After
Abert's towhee	Melozone aberti			1	1	0	0
Acorn woodpecker	Melanerpes formicivorus			6	12	0	0
Alameda song sparrow	Melospiza melodia pusillula		BCC, SSC2	1	0	0	0
Allen's hummingbird	Selasphorus sasin		BCC	9	10	0	0
American avocet	Recurvirostra americanus		BCC*	0	1	0	0
American beaver	Castor canadensis			0	1	0	0
American bittern	Botaurus lentiginosus			1	0	0	0
American coot	Fulica americana			3	5	0	0
American crow	Corvus brachyrhynchos	S		41	38	24	21	-6
American goldfinch	Spinus tristis			18	10	8	4	-10
American kestrel	Falco sparverius	R	BOP	20	22	14	5	-68
American pipit	Anthus rubescens	G		4	9	1	0
American robin	Turdus migratorius			15	12	5	1	-75
American wigeon	Mareca americana			1	3	0	0
Anna's hummingbird	Calypte anna	S		35	40	12	15	9
Ash-throated flycatcher	Myiarchus cinerascens			5	5	0	1
Baja California treefrog	Pseudacris hypochondriaca			1	2	0	0
Bald eagle	Haliaeetus leucocephalus	R	CE, BGEPA, CFP	1	1	0	1
Band-tailed pigeon	Patagioenas fasciata			1	1	0	0
Barn swallow	Hirundo rustica			9	13	5	4	-45
Bat sp.				1	0	0	0
Belted kingfisher	Ceryle alcyon			2	2	0	0
Bewick's wren	Thryomanes bewickii			10	7	1	0
Black-chinned hummingbird	Sayornis nigricans			3	1	0	0
Black-crowned night-heron	Nycticorax nycticorax			2	0	1	1
Black-headed grosbeak	Archilochus alexandri			2	1	1	0
Black-necked stilt	Nycticorax nycticorax			3	4	0	0
Black-tailed gnatcatcher	Pheucticus melanocephalus		TWL	1	1	0	0
Black-tailed jackrabbit	Himantopus mexicanus			6	3	6	1	-67
Black-throated gray warbler	Polioptila melanura			0	1	1	0
Black phoebe	Lepus californicus	S		30	22	11	9	12
Blue-gray gnatcatcher	Passerina caerulea			3	4	0	0
Blue grosbeak	Polioptila caerulea			0	1	2	0
Bobcat	Felis rufus			4	2	0	0
Botta's pocket gopher	Thomomys bottae			24	31	8	0	-100
Brewer's blackbird	Euphagus cyanocephalus			9	13	6	4	-54
Brewer's sparrow	Spizella breweri			0	1	0	0
Broad-footed mole	Scapanus latimanus			0	2	0	0
Brown-headed cowbird	Molothrus ater			9	3	3	1	0
Bryant's Savannah sparrow	Passerculus sandwichensis alaudinus	G	SSC3	1	0	0	0
Bryant's woodrat	Neotoma bryanti			1	2	0	0
Bufflehead	Bucephala albeola			2	1	0	0
Bullock's oriole	Icterus bullockii		BCC	3	2	1	0
Burrowing owl	Athene cunicularia	R, G	BCC, SSC2, BOP	3	1	1	0	-100
Bushtit	Psaltriparus minimus			19	21	4	2	-55
Cactus wren	Campylorhynchus brunneicapillus			1	1	0	0
California brown pelican	Pelicanus occidentalis californicus		CFP	1	2	0	0
California gnatcatcher	Polioptila c. californica		FT, SSC2	4	1	0	0
California ground squirrel	Otospermophilus beecheyi			18	24	8	0	-100
California gull	Larus californicus		BCC, TWL	22	15	11	10	33
California horned lark	Eremophila alpestris actia	G	TWL	1	3	0	0
California quail	Callipepla californica			6	10	3	0	-100
California scrub-jay	Aphelocoma californica			19	25	10	9	-32
California thrasher	Toxostoma redivivum		BCC	4	3	0	0
California towhee	Melozone crissalis			19	18	5	2	-58
California vole	Microtus californicus			5	5	2	0	-100
Calliope hummingbird	Stellula calliope			1	0	0	0
Canada goose	Branta canadensis			12	19	5	4	-50
Cassin's kingbird	Tyrannus vociferans			8	10	2	0	-100
Cattle egret	Bubulcus ibis			1	1	0	0
Cedar waxwing	Bombycilla cedrorum			3	4	3	0	-100
Chestnut-backed chickadee	Poecile rufescens			6	6	0	1
Chipping sparrow	Spizella passerina			1	1	1	0
Cinnamon teal	Spatula cyanoptera			1	2	0	0
Cliff swallow	Petrochelidon pyrrhonota			10	14	4	4	-29
Common goldeneye	Bucephala clangula			1	0	0	0
Common ground dove	Columbina passerina			1	0	0	0
Common merganser	Mergus merganser			1	0	0	0
Common raven	Corvus corax	S		33	34	11	10	-12
Common yellowthroat	Geothlypis trichas			2	5	0	0
Cooper's hawk	Accipiter cooperii	R	TWL, BOP	13	10	3	4	73
Costa's hummingbird	Calypte costae		BCC	1	0	0	0
Coyote	Canis latrans			15	11	2	0	-100
Dark-eyed junco	Junco hyemalis			7	7	3	1	-67
Deer mouse	Peromyscus maniculatus			0	1	0	0
Desert cottontail	Sylvilagus audubonii			12	7	2	1	-14
Domestic dog	Canis familiaris			2	0	0	0
Double-crested cormorant	Nannopterum auritum		TWL	13	10	4	2	-35
Downy woodpecker	Dryobates pubescens			2	5	1	0
Eared grebe	Podiceps nigricollis			0	1	0	0
Eastern fox squirrel	Sciurus niger			2	3	0	0
Eastern gray squirrel	Sciurus carolinensis			3	4	3	1	-75
Egyptian goose	Alopochen aegyptiacus			0	0	0	1
Eurasian collared-dove	Streptopelia decaocto	S		18	23	15	13	-32
European starling	Sturnus vulgaris	S		28	35	20	10	-60
Evening grosbeak	Coccothraustes vespertinus			0	1	1	0
Ferruginous hawk	Buteo regalis	R	TWL, BOP	0	1	2	0
Forster's tern	Sterna forstreri			1	2	0	1
Fox sparrow	Passerella iliaca			0	0	1	0
Gadwall	Anas strepera			1	1	0	0
Gambel's quail	Callipepla gambelii			1	1	0	0
Glaucous-winged gull	Larus glaucescens			1	1	1	0
Golden eagle	Aquila chrysaetos	R	BGEPA, CFP, BOP, TWL	0	2	0	0
Golden-crowned sparrow	Zonotrichia atricapilla			1	4	1	0
Gopher snake	Pituophis melanoleucus			1	0	0	0
Granite spiny lizard	Sceloporus orcutti			1	1	0	0
Grasshopper sparrow	Ammodramus savannarum	G	SSC2	1	1	0	0
Gray fox	Urocyon cinereoargenteus			2	1	0	0
Great Basin fence lizard	Sceloporus occidentalis longipes			2	2	0	0
Great blue heron	Ardea herodias			9	7	1	0
Great egret	Ardea alba			17	12	7	1	-80
Great horned owl	Bubo virginianus	R	BOP	5	3	0	0
Greater roadrunner	Geococcyx californianus			1	3	0	0
Greater white-fronted goose	Anser albifrons			1	0	0	0
Greater yellowlegs	Tringa melanoleuca			2	1	0	0
Great-tailed grackle	Quiscalus mexicanus			3	2	3	0	-100
Green heron	Butorides virescens			1	1	0	0
Hairy woodpecker	Dryobates villosus			3	4	0	0
Harbor seal	Phoca vitulina			1	1	0	0
Herring gull	Larus argentatus			2	0	2	0
Hooded merganser	Lophodytes cucullatus			0	1	0	0
Hooded oriole	Icterus cucullatus			3	5	0	0
Horned grebe	Podiceps auritus			2	0	0	0
Horned lark	Eremophila alpestris	G		4	7	0	0
House cat	Felis catus			5	3	3	2	11
House finch	Haemorphous mexicanus	S		45	46	18	12	-35
House sparrow	Passer domesticus	S		10	9	7	5	-21
House wren	Troglodytes aedon			1	2	0	0
Hutton's vireo	Vireo huttoni			3	1	1	0
Kangaroo rat	Dipodomys sp.			4	4	0	0
Killdeer	Charadrius vociferus	G		16	13	9	0	-100
Large-billed savannah sparrow	Passerculus sandwichensis rostratus		SSC2	1	0	0	0
Lark sparrow	Chondestes grammacus			2	3	0	1
Lawrence's goldfinch	Spinus lawrencei		BCC	4	2	0	0
Lazuli bunting	Passerina amoena			1	2	0	0
Least sandpiper	Calidris minutilla			1	2	0	0
Lesser goldfinch	Spinus psaltria	S		22	32	6	4	-54
Lesser nighthawk	Chordeiles acutipennis			1	2	0	0
Lesser scaup	Aythya affinis			1	0	0	0
Lesser yellowlegs	Tringa flaviceps			0	1	0	0
Lincoln's sparrow	Melospiza lincolnii			7	10	1	0
Loggerhead shrike	Lanius ludovicianus	G	SSC2	2	2	3	0	-100
Long-billed curlew	Numenius americanus		TWL, BCC*	1	0	0	0
Long-tailed weasel	Mustella frenata			1	0	0	0
MacGillivray's warbler	Geothlypus tolmiei			1	2	0	0
Mallard	Anas platyrhynchos			17	19	3	1	-70
Marsh wren	Cistothorus palustris			3	2	0	0
Merlin	Falco columbarius	R	TWL, BOP	4	1	1	0
Merriam's chipmunk	Neotamias merriami			1	0	0	0
Mew gull	Larus canus			1	0	0	0
Mouse sp.				1	0	0	0
Mountain bluebird	Sialia currucoides			1	0	0	0
Mountain chickadee	Parus gambeli			1	1	0	0
Mountain lion	Puma concolor		CCT	1	1	0	0
Mourning dove	Zenaida macroura	S		43	43	21	12	-43
Mule deer	Odocoileus hemionus			7	6	0	0
Muskrat	Ondatra zibethicus			1	0	0	0
Mute swan	Cygnus olor			1	1	1	0
Nashville warbler	Vermivora ruficapilla			1	1	0	0
Northern flicker	Colaptes auratus			10	13	6	4	-49
Northern harrier	Circus hudsonius	R, G	BCC, SSC3, BOP	8	7	0	1
Northern mockingbird	Mimus polyglottos	S		27	25	16	13	-12
Northern pintail	Anas acuta			1	0	0	0
Northern rough-winged swallow	Stelgidopteryx serripennis			15	9	4	0	-100
Nutmeg mannikin	Lonchura punctulata			2	2	0	0
Nuttall's woodpecker	Dryobates nuttallii		BCC	8	15	1	1	-47
Oak titmouse	Baeolophus inornatus		BCC	5	11	1	0
Olive-sided flycatcher	Contopus cooperi		BCC, SSC2	3	2	0	0
Orange-crowned warbler	Leiothlypis celata			3	6	1	0
Osprey	Pandion haliaetus	R	TWL, BOP	2	2	1	1
Pacific-slope flycatcher	Empidonax difficilis			1	2	0	0
Pacific wren	Troglodytes pacificus			1	2	0	0
Pelagic cormorant	Phalacrocorax pelagicus			0	1	0	0
Peregrine falcon	Falco peregrinus	R	CFP, BOP	5	3	1	1
Phainopepla	Phainopepla nitens			2	3	0	0
Pied-billed grebe	Podilymbus podiceps			2	1	0	0
Prairie falcon	Falco mexicanus	R, G	TWL, BOP	1	0	1	0
Purple finch	Haemorhous purpureus			2	2	1	0
Pygmy nuthatch	Sitta pygmaea			1	2	0	0
Raccoon	Procyon lotor			3	3	1	0
Red-breasted nuthatch	Sitta canadensis			1	3	0	0
Red-breasted sapsucker	Sphyrapicus ruber			1	0	0	0
Red-masked parakeet	Psittacara erythrogenys			0	1	0	0
Red-necked phalarope	Phalaropus lobatus			0	1	0	0
Red-shouldered hawk	Buteo lineatus	R	BOP	7	3	4	1	-42
Red-tailed hawk	Buteo jamaicensis	R	BOP	35	44	17	7	-67
Red-winged blackbird	Agelaius phoeniceus			14	16	6	1	-85
Red fox	Vulpes vulpes			1	1	0	0
Ring-billed gull	Larus delawarensis			4	7	0	0
Ring-necked pheasant	Phasianus colchicus			2	0	0	0
Rock pigeon	Columba livia	S	Non-native	21	31	18	15	-44
Rose-ringed parakeet	Psittacula krameri			0	2	0	0
Ruby-crowned kinglet	Regulus calendula			6	12	1	3	50
Ruddy duck	Oxyura jamaicensis			0	1	0	0
Rufous hummingbird	Selasphorus rufus		BCC			1	0
San Diegan tiger whiptail	Aspidoscelis tigris stejnegeri		SSC	1	0	0	0
San Francisco common yellowthroat	Geothlypis trichas sinuosa		BCC, SSC3	1	1	0	0
San Francisco dusky-footed woodrat	Neotoma fuscipes annectens		SSC	1	1	0	0
Savannah sparrow	Passerculus sandwichensis	G		13	14	5	2	-63
Say's phoebe	Sayornis saya	G		21	15	6	4	-7
Sharp-shinned hawk	Accipiter striatus	R	TWL, BOP	1	2	0	1
Short-billed dowitcher	Limnodromus griseus		BCC	1	0	0	0
Short-eared owl	Asio flammeus	R	BCC, SSC3, BOP	2	0	0	0
Side-blotched lizard	Uta stansburiana			1	0	0	0
Sierran treefrog	Pseudacris sierra			3	9	1	0	-100
Snow goose	Chen caerulescens			1	1	0	0
Snowy egret	Egretta thula			3	4	0	1
Song sparrow	Melospiza melodia			13	15	0	0
Sora	Porzana carolina			1	0	0	0
Southern alligator lizard	Gerrhonotus multicarinatus			2	1	0	0
Southern California rufous-crowned sparrow	Aimophila ruficeps canescens		TWL	2	0	0	0
Southern mule deer	Odocoileus hemionus fuliginatus			1	1	0	0
Southern Pacific rattlesnake	Crotalus oreganus helleri			1	1	0	0
Southern sagebrush lizard	Sceloporus graciosus vandenburgianus			1	0	0	0
Spotted sandpiper	Actitis macularius			1	1	0	0
Spotted towhee	Pipilo maculatus			10	11	0	0
Steller's jay	Cyanocitta stelleri			4	3	0	0
Striped skunk	Mephitis mephitis			3	3	0	0
Surf scoter	Melanitta perspicillata			0	1	0	0
Swainson's hawk	Buteo swainsoni	R	CT, BOP	5	3	3	1	-44
Swainson's thrush	Catharus ustulatus			1	1	0	0
Townsend's warbler	Setophaga townsendi			1	2	1	0
Tree swallow	Tachycineta bicolor			6	6	0	1
Tricolored blackbird	Agelaius tricolor	G	CT, BCC, SSC1	2	0	0	0
Tundra swan	Cygnus columbianus			1	0	0	0
Turkey vulture	Cathartes aura	R	BOP	17	21	11	7	-49
Vaux's swift	Chaetura vauxi		SSC2, BCC	0	1	0	0
Verdin	Auriparus flaviceps		BCC	1	1	0	0
Vermilion flycatcher	Pyrocephalus rubinus	G	SSC2	1	0	0	0
Violet-green swallow	Tachycineta thalassina			3	4	0	1
Virginia opossum	Didelphis virginianus			2	0	0	0
Warbling vireo	Vireo gilvus			2	1	0	0
Western bluebird	Sialia mexicana			9	12	0	0
Western fence lizard	Sceloporus occidentalis			2	6	2	1	-83
Western gray squirrel	Sciurus griseus			3	5	0	1
Western gull	Larus occidentalis		BCC	2	6	1	1	-67
Western kingbird	Tyrannus verticalis			10	9	4	2	-44
Western meadowlark	Sturnella neglecta	G		18	23	11	2	-86
Western sandpiper	Calidris mauri			0	1	0	0
Western screech-owl	Megascops kennicottii	R	BOP	1	0	0	0
Western side-blotched lizard	Uta stansburiana elegans			4	5	0	0
Western tanager	Piranga ludoviciana			2	2	0	0
White-breasted nuthatch	Sitta carolinensis			4	6	0	0
White-crowned sparrow	Zonotrichia leucophrys			17	23	9	4	-67
White-faced ibis	Plegadis chihi		TWL	1	3	1	0
White-tailed kite	Elanus leucurus	R	CFP, BOP	14	7	7	0	-100
White-throated swift	Aeronautes saxatalis			5	3	2	0	-100
White-winged dove	Zenaida asiatica			1	0	0	0
Wild turkey	Meleagris gallopavo			6	7	2	0	-100
Willet	Tringa semipalmata		BCC	3	3	0	0
Willow flycatcher	Empidonax traillii		CE	0	1	1	0
Wilson's snipe	Gallinago delicata			0	2	1	0
Wilsons warbler	Wilsonia pusilla			3	6	0	0
Wood duck	Aix sponsa			1	0	0	0
Wrentit	Chamaea fasciata		BCC	5	6	0	0
Yellow-billed magpie	Setophaga petechia		BCC	2	1	3	0	-100
Yellow-headed blackbird	Pica nuttalli	G	SSC3	1	0	0	0
Yellow-rumped warbler	Xanthocephalus xanthocephalus	S		21	21	7	10	43
Yellow warbler	Setophaga coronata		SSC2	5	6	0	2

¹ G = grassland bird, R = raptor, S = synanthrope. ² FT or FE = federal threatened or endangered, FC = federal candidate for listing as threatened or endangered, BGEPA = Bald and Golden Eagle Protection Act, BCC = U.S. Fish and Wildlife Service’s Birds of Conservation Concern, CT or CE = California threatened or endangered, CCT or CCE = Candidate California threatened or endangered, CFP = California Fully Protected (California Fish and Game Code 3511), SSC = California Species of Special Concern (not threatened with extinction, but rare, very restricted in range, declining throughout range, peripheral portion of species' range, associated with habitat that is declining in extent), SSC₁, SSC₂ and SSC₃ = California Bird Species of Special Concern priorities 1, 2 and 3, respectively (Shuford and Gardali 2008), TWL = California Taxa to Watch List, and BOP = Birds of Prey (California Fish and Game Code 3503.5). ^* Uncertain of range of BCC status based on 2021 Bird of Conservation Concern list.

4. Discussion

Assuming that our experimental tests sufficiently controlled for differences in size, condition, and setting between control and impact sites, and for differences in survey duration, our experiment revealed substantial reductions in vertebrate species richness and numerical abundance caused by development. Although our surveys likely failed to detect all of the species or to count all of the animals available at the times of our surveys, we believe it is unlikely that underlying survey biases could have substantially confounded the magnitudes of development impacts we measured. We suggest, for example, that survey bias cannot explain the 74% decline we measured in the number of vertebrate species that we uniquely detected at a site in one phase relative to the species we detected in the other phase. The magnitude of this effect was too large to be explained by anything other than a profound shift in the species composition of project sites following development. Site-specific project impacts are generally devastating to wildlife.

Only 7 species of wildlife increased in frequency of detection among surveys at sites where development preceded our surveys in the after phase of our experiment. Of these 7 species, 2 were generalists – California gull and yellow-rumped warbler, consistent with the finding that generalist species of birds were most often the species that adapt to urbanized landscapes [38]. Black phoebe, house cat, and Anna’s hummingbird were 3 other species that increased in frequency of occurrence, but their increases were small. Ruby-crowned kinglet’s increase remains questionable, considering the small sample sizes, but Cooper’s hawk is a specialized forager that appears to capitalize on urbanization. Otherwise, the majority of wildlife species with sufficient sample sizes declined in their numbers of detection among our surveys at sites where buildings were constructed in the time period between our before and after surveys. We suggest that the categorization of wildlife as urban avoiders, urban adapters and urban exploiters [39] provides a useful framework for understanding how wildlife respond to urbanization, but we also suggest that most of the urban adapters and urban exploiters can be vulnerable to the final stage of development at a given site.

Whereas invasive species and synanthropic species might fare better than native species in urban environments [40], we found that species in both of these groups also declined after development of project sites, similar to the finding of Scott [29]. The declines of species in these groups were not as great as for raptors and grassland bird species, but they were nevertheless substantial. On the whole, development projects reduced species richness and wildlife abundance.

Terrestrial vertebrate species declined the most in our study, consistent with previous findings [41,42,43], but the declines we measured were not significant due to insufficient statistical power. Though not statistically significant, we suggest that our measured declines ought to be considered biologically significant. In the field, finding fewer or no terrestrial mammals and amphibians where we had seen them before was noticeable, and we assert these declines resulted directly from development. Some of these terrestrial vertebrate species were ecological keystone species, such as the Botta’s pocket gopher in Figure 16 and California ground squirrel. California ground squirrel, in particular, has been found to limit the distribution of multiple special-status species such as burrowing owl [44] and loggerhead shrike [45]. Indeed, where development preceded our second surveys, California ground squirrels were not observed and neither were any of the burrowing owls or loggerhead shrikes that we had seen at those sites prior to development (Figure 17).

Andrén [46] predicted that “landscapes with highly fragmented habitat, patch size and isolation will complement the effect of habitat loss and the loss of species or decline in population size will be greater than expected from habitat loss alone.” Our results tended to support this prediction. Our mean ANOVA residuals of the number of vertebrate species detected at one hour of survey was negative among sites in urban infill and redevelopment settings, and positive among sites surrounded by open space (Figure 15a), meaning there were relatively fewer species in urban settings and relatively more in settings of open space. This result resembles that of another study that found that bird species richness in urban settings correlated positively with bird species richness in adjacent landscapes composed of managed or natural vegetation [47].

We note, also, that we detected more species composed of smaller average counts of individuals in the before phase of control sites as compared to the before phase of impact sites – the sites that were to be developed later during our study, or alternatively, we found smaller numbers of species of larger average counts at impact sites even in the before phase, which was a pattern previously noted [29]. By the time we initiated our first surveys at the impact sites, the impact sites were already different in species composition. In fact, the impact sites differed from control sites with their average smaller size and lower elevation, but perhaps more importantly with their lower connectivity to open space, their higher average rank on the urban setting index, their higher average ratings for level of disturbance, and their average higher intensity of actions resulting in suppression of wildlife occurrences. We also surveyed impact sites more briefly than we surveyed control sites, but our briefer surveys probably reflected the smaller average size of impact sites. Earlier in our study, we could not have predicted which sites would be developed sooner than other sites, but now it appears that smaller infill sites tend to be managed more aggressively to suppress wildlife, tend to support fewer species, and are more likely to be approved for development.

Numerous species of vertebrate wildlife were found only at control sites, further revealing the potential biological difference that already existed by the time of our first surveys at project sites, but which also prevented species-specific measures of development effect. Such species included Allen’s hummingbird and black-chinned hummingbird, American coot, Black-necked stilt, blue-gray gnatcatcher and California gnatcatcher, bobcat, California thrasher, common yellowthroat, great horned owl, Hairy woodpecker, hooded oriole, horned lark, marsh wren, mule deer, olive-sided flycatcher, ring-billed gull, song sparrow, striped skunk, western bluebird, white-breasted nuthatch and Wilson’s warbler. A mitigating factor in our findings of these species only at control sites was the fact that we surveyed twice the number of control sites as compared to impact sites.

Before- and after-phase surveys at control sites revealed a trend that was likely more indicative of cumulative effects at landscape scales, as these surveys were of equal number and free of on-site development. Despite our average detections of about 2–3 additional species after our second surveys at control sites, and despite our average counts of 26% more birds in the absence of site-specific development, in our follow-up surveys we detected 56% fewer special-status species and we counted 44% fewer animals of special-status species. During our study, special-status species of vertebrate wildlife appeared to have been on the decline within the regions of our study. These declines indicate that project-specific mitigation measures have been failing to avoid cumulative impacts.

We endeavored to design and implement our study to minimize the effects of bias and error by standardizing site-specific survey dates, start times, survey duration, and survey methods. Where we walked a transect along the perimeter of a site during the first survey, we tried to repeat the walk of the same transect during the second survey. We surveyed most sites the second time with the same investigator, or by both of us, as we had surveyed the first time. But there was variation in survey methods between sites, most notably survey duration. We attempted to adjust our survey findings for variation in survey duration by best-fitting a nonlinear model to each survey’s increase in the cumulative number of detected species with increasing time into the survey. From each model, we predicted the number of species detected after one hour of survey, which was enough time to predict a substantial number of species but also well within the range of survey durations that we completed among the project sites. Nevertheless, the ANOVA model’s residuals derived from model-predicted numbers of species detected after 1 hour continued to increase weakly with increasing survey duration among project sites (Figure 14b). We did not entirely eliminate the effects of survey duration. We do not believe that the remaining effect of survey duration significantly affected our study results, but we note this effect for designing future studies of the effects of urbanization on wildlife. The effect was stronger without the model-based adjustments, but the model-based adjustments appear to have been sample-dependent. The duration of survey affects the pattern of increasing cumulative number of species with increasing survey duration, and hence affects the nonlinear model fit to the pattern. It might be possible to standardize the pattern in the cumulative number of species detected by standardizing Walker’s [31] results-based stopping rule, or by standardizing survey duration [33]. If the latter, then we recommend a relatively long survey duration such as 2 or more hours.

Another potential bias is the change in detection rates of wildlife species after the project sites were developed. Our probability of detecting the average bird was likely higher after the available perches transitioned from trees and shrubs to light standards on parking lots and the rooflines of warehouses. Detection likelihood might have increased after opportunities to view flying birds transitioned from views of complex environmental backgrounds to the walls of warehouses, although the environmental backgrounds of most of our project sites were rather simple. At some sites, the landscaping around warehouses and other new structures comprising the project might have been simpler than the preconstruction environments, thus better enabling us to detect an animal on those portions of the project site had the animals been present. Although we acknowledge this potential bias, the amount of survey time we committed to each site gave us ample opportunity to detect the species of wildlife that were truly there at the times of both of our surveys. At developed sites, our rates of detection of wildlife species were much slower (Figure 8) and the total numbers of species detected were many fewer (Table 2 and Table 3), but we believe that these differences more reflected biological conditions than they did our survey detection rates.

Another potential bias was the differential detection rates among species of wildlife. We likely disproportionately detected the most readily detectable species while failing to detect those species that are smallest in body size, nocturnal, fossorial and more cryptic. Furthermore, the numerical abundances we attributed to the species we detected likely often differed from the true numerical abundances even within the spatial and temporal scopes of our surveys. Whereas our counts of animals might have more often approached the true numbers of the largest-bodied species such as red-tailed hawks (Buteo jamaicensis) and mule deer (Odocoileus hemionus), they likely under-represented the smaller-bodied species such as sparrows and warblers and western fence lizards and Belding’s orange-throated whiptails.

5. Conclusions

We found that special-status species declined on control sites even in the absence of site-specific development, which indicates widespread ineffectiveness of project-level mitigation measures and cumulative impacts from regionwide urbanization. We also found that development projects directly reduce vertebrate wildlife species richness and wildlife abundance, and that these reductions are large. Vertebrate wildlife species most affected by development in California are terrestrial species, as well as grassland birds, raptors and special-status species. More needs to be learned as soon as possible about the impacts of urbanization on wildlife.

To follow up on Marzluff’s [17] recommendation, and to actually take advantage of the opportunities to measure the effects of development projects on wildlife, the California Environmental Quality Act should be amended to require that reconnaissance surveys be repeated in each season of the year preceding the public circulation of an environmental review document. The required mitigation plan should include funding for post-construction reconnaissance surveys of the same methods, number, and seasonal spacing to more robustly represent the wildlife community before and after development. CEQA should be further amended to require a sufficient funding allocation from each project applicant that would be directed to cumulative effects monitoring. Whereas CEQA requires a cumulative impacts analysis, data needed to analyze cumulative impacts to wildlife usually do not exist, thus consultants' analyses of cumulative impacts are speculative. Because long-term monitoring is often not required, thus not performed, the consultants' conclusions of cumulative impacts cannot be confirmed nor denied. Long-term monitoring would give all parties involved a better understanding of how to analyze cumulative impacts, because we would have a better understanding of how development truly affects each species. A cumulative impacts fund should be administered by a trusted party to ensure that unbiased, qualified biologists implement long-term monitoring of wildlife within a spatial area that can meaningfully inform of cumulative effects.

To soften the impacts of urbanization on wildlife, CEQA should be amended to require the use of native and xeric-adapted plants in landscaping. i.e., chaparral, grassland, and locally appropriate scrub plants as opposed to landscaping with lawn and exotic shrubs. Native plants offer more structure, cover, food resources, and nesting substrate for wildlife than landscaping with lawn. Native plant landscaping has been shown to increase the abundance of arthropods which act as importance sources of food for vertebrate wildlife and are crucial for pollination and plant reproduction [48,49]. Further, many endangered and threated insects require native host plants for reproduction and migration, e.g., monarch butterfly. Around the world, landscaping with native plants over exotic plants increases the abundance and diversity of birds, and is particularly valuable to native birds [50,51,52,53]. Landscaping with native plants is a way to maintain or to bring back some of the natural habitat and lessen the footprint of urbanization by acting as interconnected patches of habitat for wildlife [54,55]. Potentially more effective would be to preserve the original soil-vegetation complexes within the project footprint wherever impervious surfaces are not absolutely necessary.

CEQA should also be amended to require project applicants contribute funding to wildlife rehabilitation facilities. As projects are built, and wildlife are subsequently injured by the windows of buildings, project-generated traffic, and free-ranging house cats of new residents, wildlife rehabilitation facilities should be provided the resources they need to attempt to rectify these types of project impacts.